With the rapid growth of industries like e-commerce, food delivery, and ride-hailing, research on the Vehicle Routing Problem (VRP) is becoming increasingly relevant. However, most of the research in the field of VRP is based on static delivery tasks. In such tasks, the information about customer and order requests is provided before the delivery vehicle departs from the depot, and it remains constant during delivery. However, in the real world, the delivery tasks are often dynamic, where only some orders are known before the vehicle departs from the depot, and the rest of the orders are disclosed over time [1].

Imitation learning aims to reproduce expert behaviors without relying on an explicit reward signal. However, real-world demonstrations often present challenges, such as multi-modal, data imbalance, and expensive labeling processes. In this work, we propose a novel semi-supervised imitation learning architecture that learns disentangled behavior representations from imbalanced demonstrations using limited labeled data. Specifically, our method consists of three key components. First, we adapt the concept of semi-supervised generative adversarial networks to the imitation learning context. Second, we employ a learnable latent distribution to align the generated and expert data distributions. Finally, we utilize a regularized information maximization approach in conjunction with an approximate label prior to further improve the semi-supervised learning performance. Experimental results demonstrate the efficiency of our method in learning multi-modal behaviors from imbalanced demonstrations compared to baseline methods.

This paper addresses the problem of legged locomotion in unstructured environments, and a novel Hierarchical multi-contact motion planning method for hexapod robots is proposed by combining Free Gait motion planning and Deep Reinforcement Learning (HFG-DRL). We structurally decompose the complex free gait multi-contact motion planning task into path planning in discrete state space and gait planning in continuous state space. Firstly, the Soft Deep Q-Network (SDQN) is used to obtain the global prior path information in the Path Planner (PP). Secondly, a Free Gait Planner (FGP) is proposed to obtain the gait sequence. Finally, based on the PP and the FGP, the Center-of-Mass (CoM) sequence is generated by the trained optimal policy using the designed Deep Reinforcement Learning (DRL) algorithm. Experimental results in different environments demonstrate the feasibility, effectiveness, and advancement of the proposed method. Videos are shown athttp://www.hexapod.cn/hfg-drl.html.

Legged locomotion in a complex environment requires careful planning of the footholds of legged robots. In this paper, a novel Deep Reinforcement Learning (DRL) method is proposed to implement multi-contact motion planning for hexapod robots moving on uneven plum-blossom piles. First, the motion of hexapod robots is formulated as a Markov Decision Process (MDP) with a specified reward function. Second, a transition feasibility model is proposed for hexapod robots, which describes the feasibility of the state transition under the condition of satisfying kinematics and dynamics, and in turn determines the rewards. Third, the footholds and Center-of-Mass (CoM) sequences are sampled from a diagonal Gaussian distribution and the sequences are optimized through learning the optimal policies using the designed DRL algorithm. Both of the simulation and experimental results on physical systems demonstrate the feasibility and efficiency of the proposed method. Videos are shown at https://videoviewpage.wixsite.com/mcrl.